Signal Processing Method and Apparatus Based on Spiking Neural Network

ABSTRACT

A signal processing method and apparatus includes determining a first signal F 1 (t) output by a first neuron, processing the first signal F 1 (t) using q orders of synapse weight parameters w q (t), w q−1 (t), . . . , w 1  (t) to obtain a second signal F 2 (t), and inputting the second signal F 2 (t) to a second neuron, where the second neuron is a next-layer neuron of the first neuron.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2016/107419, filed on Nov. 28, 2016, the disclosure of which ishereby incorporated by reference in its entirety.

TECHNICAL FIELD

This application relates to the field of information technologies, andin particular, to a signal processing method and apparatus based on aspiking neural network.

BACKGROUND

A neural network is a computing system simulating a structure of abiological brain to process data. A large quantity of neurons (nervecells) are complexly intertwined inside the biological brain, and aformer neuron (a dendrite) is connected to a latter neuron (an axon)using a synapse structure to transfer information. Correspondingly, eachnode in a structure of the neural network may simulate one neuron andexecute a specific operation, for example, an activation function. Aconnection between nodes is used to simulate a neural synapse, and aweight of the synapse represents strength of a connection between twoneurons. The neural network has a strong nonlinear, adaptive, andfault-tolerant information processing capability.

An emerging spiking neural network may desirably resolve a real-timeinformation processing problem. In the spiking neural network,information is transferred using a spatio-temporal information codingscheme for a spike signal of a neuron. An input behavior of the networkis that a neuron receives a spike signal, and an output behavior of thenetwork is that a neuron sends a spike signal. Neurons operate inparallel. Therefore, an operation manner of the spiking neural networkis closer to that of a real biological system.

Spike-timing-dependent plasticity (STDP) is a relatively common learningmethod in the spiking neural network. The spike-timing-dependentplasticity describes a rule of adjusting a synapse weight betweenneurons If information about another neuron is generated before anactivity of a current neuron, a connection between the another neuronand the current neuron is enhanced, or if information about anotherneuron is generated after an activity of a current neuron, a connectionbetween the another neuron and the current neuron is weakened. Thelearning rule can desirably resolve a time-related informationprocessing problem in the spiking neural network. Therefore, thereal-time problem can be desirably processed.

In addition, the biological brain has a forgetting characteristic duringlearning. This is mainly because storage space of the brain is limited,and it is impossible for the brain to permanently store all receivedinformation. Therefore, some irrelevant information needs to beforgotten to improve information storage and processing efficiency. Onthe other hand, due to the forgetting characteristic, a learnedunimportant feature or a learned interference signal feature may be“discarded”, and an important feature of a thing is “stored” for long.Therefore, the forgetting characteristic of the biological brain is ofgreat significance in actual application of the neural network. However,current development of a spiking neural network is still at an earlystage, and a spiking neural network having a forgetting characteristichas not been implemented. Consequently, an existing spiking neuralnetwork needs relatively large information storage space, and processingefficiency is low, a key feature cannot be extracted, and the actualapplication is hindered.

SUMMARY

This application provides a signal processing method and apparatus basedon a spiking neural network, to simulate a neural network based on aforgetting characteristic such that the neural network is moreconsistent with reality.

According to a first aspect, a signal processing method based on aspiking neural network is provided. The method includes determining afirst signal F₁(t) output by a first neuron, processing the first signalF₁(t) using q orders of synapse weight parameters w_(q)(t), w_(q−1)(t),. . . , w₁(t), to obtain a second signal F₂(t), where a speed at whichan initial function w_(x+1) ⁰(t) met by an (x+1)^(th)-order synapseweight parameter of the q orders of synapse weight parameters attenuateswith time t is higher than a speed at which an initial function w_(x)⁰(t) met by an x^(th)-order synapse weight parameter attenuates with thetime t, q is a positive integer greater than 1, and 1≤x≤q−1, andinputting the second signal F₂(t) to a second neuron, where the secondneuron is a next-layer neuron of the first neuron.

Therefore, in the signal processing method based on a spiking neuralnetwork in this embodiment of this application, an input signal isprocessed using a plurality of orders of synapse weight parameters, andinitial functions of the orders of the synapse weight parametersattenuate with time at different speeds such that a forgettingcharacteristic of the neural network is simulated, some unimportantinformation such as a secondary feature or background noise isforgotten, the neural network is more consistent with reality, featureextraction becomes easier, and a problem of an excessively largeinformation storage amount is further resolved.

It should be understood that the first signal F₁(t) is a spike signal.

It should be understood that an initial function is set for each orderof synapse weight parameter of the q orders of synapse weight parametersw_(q)(t), w_(q−1)(t), . . . , w₁(t), and each order of synapse weightparameter meets the corresponding initial function when each order ofsynapse weight parameter is not stimulated.

It should be understood that a type of an initial function correspondingto any order of synapse weight parameter of the q orders of synapseweight parameters w_(q)(t), w_(q−1)(t), . . . , w₁(t) may be a nonlinearattenuation function such as an exponential function, a logarithmicfunction, or a step function, or may be a linear attenuation function,or may be a combination of any two or more of the foregoing functiontypes.

With reference to the first aspect, in an implementation of the firstaspect, an initial function w₁ ⁰(t) of a first-order synapse weightparameter of the q orders of synapse weight parameters does notattenuate with time.

With reference to the first aspect and the foregoing implementation, inanother implementation of the first aspect, the first signal F₁(t)includes a first sub-signal F₁(t₁) output by the first neuron at amoment t₁, and a q^(th)-order synapse weight parameter w_(q)(t₁) at themoment t₁ meets a condition (1):

w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (1)

where w_(q)(t₁−1) represents a q^(th)-order synapse weight parameter ata moment t₁−1, the moment t₁−1 is a previous moment of the moment t₁,and ΔF_(q)(t₁) is an update quantity determined based on a learning ruleand a stimulation effect, at the moment t₁, of the first sub-signalF₁(t₁) on the q^(th)-order synapse weight parameter w_(q)(t₁−1) at themoment t₁−1, and an x^(th)-order synapse weight parameter w_(x)(t₁) atthe moment t₁ meets a condition (2):

w _(x)(t ₁)=w _(x)(t ₁−1)+ΔF _(x)(t ₁)+Δw _(x,x+1)(t ₁)+Δw _(x,x+2)(t₁)+ . . . +Δw _(x,q)(t ₁)  (2)

where w_(x)(t₁−1) represents an x^(th)-order synapse weight parameter atthe moment t₁−1, ΔF_(x)(t₁) is an update quantity determined based onthe learning rule and a stimulation effect, at the moment t₁, of thefirst sub-signal F₁(t₁) on the x^(th)-order synapse weight parameterw_(x)(t₁−1) at the moment t₁−1, Δw_(x,x+1)(t₁), Δw_(x,x+2)(t₁), . . . ,Δw_(x,q)(t₁) are respectively quantities of impact of an(x+1)^(th)-order synapse weight parameter, an (x+2)^(th)-order synapseweight parameter, . . . , and the q^(th)-order synapse weight parameterthat are at the moment t₁ on the x^(th)-order synapse weight parameterw_(x)(t₁).

It should be understood that the moment t₁ may be any moment in a timeperiod within which the first neuron outputs the first signal F₁(t), andthe first signal F₁(t) includes the first sub-signal F₁(t₁) output atthe moment t₁.

It should be understood that for a moment t₂ after the time periodwithin which the first neuron outputs the first signal F₁(t), that themoment t₂ is any moment at which there is no stimulation effect of thefirst signal F₁(t), the q^(th)-order synapse weight parameter meets aninitial function w_(q) ⁰(t₂) at the moment t₂ .

With reference to the first aspect and the foregoing implementations, inanother implementation of the first aspect, when an (x+i)^(th)-ordersynapse weight parameter w_(x+i)(t₁) at the moment t₁ is greater than orequal to a threshold of an (x+i)^(th)-order synapse weight,Δw_(x,x+i)(t₁) is not 0, or when an (x+i)^(th)-order synapse weightparameter w_(x+i)(t₁) at the moment t₁ is less than a threshold of an(x+i)^(th)-order synapse weight, Δw_(x,x+i)(t₁) is equal to 0, and i=1,2, . . . , or q−x.

It should be understood that for a moment t₂ after a time period withinwhich the first neuron outputs the first signal F₁(t), assuming that themoment t₂ is any moment at which there is no stimulation effect of thefirst signal F₁(t), a quantity ΔF_(x)(t₂) of impact of the first signalF₁(t) on an x^(th)-order synapse weight parameter at the moment t₂ is 0.If quantities Δw_(x,x+1)(t₂), Δw_(x,x+2)(t₂), . . . , Δw_(x,q)(t₂) ofimpact of an (x+1)^(th)-order synapse weight parameter, an(x+2)^(th)-order synapse weight parameter, . . . , and the q^(th)-ordersynapse weight parameter on the x^(th)-order synapse weight parameterare not all 0 in this case, the x^(th)-order synapse weight parameterstill meets the condition (2) at the moment t₂, or if quantitiesΔw_(x,x+1)(t₂), Δw_(x,x+2)(t₂), . . . , Δw_(x,q)(t₂) of impact of an(x+1)^(th)-order synapse weight parameter, an (x+2)^(th)-order synapseweight parameter, . . . , and the q^(th)-order synapse weight parameteron the x^(th)-order synapse weight parameter are all equal to 0 in thiscase, the x^(th)-order synapse weight parameter meets an initialfunction w_(x) ⁰(t₂) at the moment t₂.

It should be understood that the first sub-signal F₁(t₁) at the momentt₁ may be set to affect all or some of the q orders of synapse weightparameters. For example, the first sub-signal F₁(t₁) may be set toaffect only the q^(th)-order synapse weight parameter. In other words, aquantity ΔF_(q)(t₁ ) of impact of the first sub-signal F₁(t₁) onq^(th)-order synapse weight parameter is not 0, and a quantityΔF_(x)(t₁) of impact of the first sub-signal F₁(t₁) on the x^(th)-ordersynapse weight parameter other than the q^(th)-order synapse weightparameter is 0. For another example, the first sub-signal F₁(t₁) mayalternatively be set to affect only the q^(th)-order synapse weightparameter to a (q−x)^(th)-order synapse weight parameter. In otherwords, ΔF_(q)(t₁), ΔF_(q−1)(t₁), . . . , and ΔF_(q−x)(t₁) and are not 0,and ΔF_(q−x−1)(t₁), ΔF_(q−x−2)(t₁), . . . , and ΔF₁(t₁) are 0.

Optionally, for the condition (2), the x^(th)-order synapse weightparameter may be set to be related to each higher-order synapse weightparameter. That is, the (x+1)^(th)-order synapse weight parameteraffects each lower-order synapse weight parameter. Alternatively, the(x+1)^(th)-order synapse weight parameter may be set to affect only somelower-order synapse weight parameters. For example, the (x+1)^(th)-ordersynapse weight parameter may be set to affect only the x^(th)-ordersynapse weight parameter that is one order lower than the(x+1)^(th)-order synapse weight parameter. In other words, thex^(th)-order synapse weight parameter is related to the (x+1)^(th)-ordersynapse weight parameter. In other words, Δw_(x,x+2)(t₂),Δw_(x,x+3)(t₂), . . . , Δw_(x,q)(t₂) are all 0 in the condition (2). Foranother example, the x^(th)-order synapse weight parameter mayalternatively be set to be related to the (x+1)^(th)-order synapseweight parameter to the (x+i)^(th)-order synapse weight parameter.Therefore, Δw_(x,x+i+1)(t₂ ), Δw_(x,x+i+2)(t₂), . . . , Δw_(x,q)(t₂) areall 0.

With reference to the first aspect and the foregoing implementations, inanother implementation of the first aspect, the first signal F₁(t)includes a first sub-signal F₁(t₁) output by the first neuron at amoment t₁, and a q^(th)-order synapse weight parameter w_(q)(t₁) at themoment t₁ meets a condition (3):

w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (3)

where w_(q)(t₁−1) represents a q^(th)-order synapse weight parameter ata moment t₁−1, the moment t₁−1 a previous moment of the moment t₁, andΔF_(q)(t₁) is an update quantity determined based on a learning rule anda stimulation effect, at the moment t₁, of the first sub-signal F₁(t₁)on the q^(th)-order synapse weight parameter w_(q)(t₁−1) at the momentt₁−1, and when an (x+1)^(th)-order synapse weight parameter at themoment t₁ is greater than or equal to a threshold of an (x+1)^(th)-ordersynapse weight, an x^(th)-order synapse weight parameter w_(x)(t₁) atthe moment t₁ meets a condition (4):

w _(x)(t ₁)=w _(x)(t ₁−1)+Δw _(x,x+1)(t ₁)  (4)

where w_(x)(t₁−1) represents an x^(th)-order synapse weight parameter atthe moment t₁−1, Δw_(x,x+1)(t₁) is a quantity of impact of the(x+1)^(th)-order synapse weight parameter at the moment t₁ on thex^(th)-order synapse weight parameter w_(x)(t₁), or when an(x+1)^(th)-order synapse weight parameter w_(x+1)(t₁) the moment t₁ isless than a threshold of an (x+1)^(th)-order synapse weight, anx^(th)-order synapse weight parameter w_(x)(t₁) at the moment t₁ meetsan initial function w_(x) ⁰ (t₁).

With reference to the first aspect and the foregoing implementations, inanother implementation of the first aspect, the learning rule is alearning rule based on a biological feature or a supervised learningrule based on an error back propagation mechanism.

The learning rule based on a biological feature may be, for example, anSTDP learning rule or a Hebb learning rule. The supervised learningalgorithm based on the error back propagation mechanism may be, forexample, a SpikeProp learning rule, a QucikProp learning rule, aTempotron learning rule, or an E-Learning learning rule.

With reference to the first aspect and the foregoing implementations, inanother implementation of the first aspect, the processing the firstsignal F₁(t) using q orders of synapse weight parameters w_(q)(t),w_(q−1)(t), . . . , w₁(t), to obtain a second signal F₂(t) includesdetermining a product of the first signal F₁(t) and the first-ordersynapse weight parameter w₁(t) as the second signal F₂(t).

According to a second aspect, a signal processing apparatus based on aspiking neural network is provided, configured to perform the method inthe first aspect or any possible implementation of the first aspect. Theapparatus includes units configured to perform the method in the firstaspect or any possible implementation of the first aspect.

According to a third aspect, a signal processing apparatus based on aspiking neural network is provided, including a memory and a processor.The memory is configured to store an instruction. The processor isconfigured to execute the instruction stored in the memory, and when theprocessor executes the instruction stored in the memory, the processorperforms the method in the first aspect or any possible implementationof the first aspect.

According to a fourth aspect, a computer readable medium is provided,configured to store a computer program, where the computer programincludes an instruction used to perform the method in the first aspector any possible implementation of the first aspect.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram of a spiking neural network according toan embodiment of the present disclosure.

FIG. 2 is a schematic flowchart of a signal processing method based on aspiking neural network according to an embodiment of the presentdisclosure.

FIG. 3 is a schematic diagram of a memory effect corresponding to eachorder of asynapse weight parameter according to an embodiment of thepresent disclosure.

FIG. 4 is a schematic diagram of a form of a connection between twoneurons according to an embodiment of the present disclosure.

FIG. 5 is a schematic diagram of simulating a synapse weight parameterby a memristor having a two-order effect according to an embodiment ofthe present disclosure.

FIG. 6 is a schematic diagram of an application instance of two ordersof synapse weight parameters according to an embodiment of the presentdisclosure.

FIG. 7 is a schematic diagram of another application instance of twoorders of synapse weight parameters according to an embodiment of thepresent disclosure.

FIG. 8 is a schematic diagram of still another application instance oftwo orders of synapse weight parameters according to an embodiment ofthe present disclosure.

FIG. 9 is a schematic diagram of a curve of an STDP learning ruleaccording to an embodiment of the present disclosure.

FIG. 10 is a schematic diagram of an application instance of threeorders of synapse weight parameters according to an embodiment of thepresent disclosure.

FIG. 11 is a schematic diagram of two orders of synapse weightparameters used for word memorization according to an embodiment of thepresent disclosure.

FIG. 12 is a schematic block diagram of a signal processing apparatusbased on a spiking neural network according to an embodiment of thepresent disclosure.

FIG. 13 is a schematic block diagram of a signal processing apparatusbased on a spiking neural network according to another embodiment of thepresent disclosure.

DESCRIPTION OF EMBODIMENTS

The following describes technical solutions in the embodiments of thepresent disclosure with reference to the accompanying drawings.

FIG. 1 is a schematic diagram of a spiking neural network according toan embodiment of the present disclosure. As shown in FIG. 1, ak^(th)-layer neuron is a neuron at any layer in the spiking neuralnetwork, the k^(th)-layer neuron may include m neurons, and m is apositive integer. A (k+1)^(th)-layer neuron is a next-layer neuron ofthe k^(th)-layer neuron, the (k+1)^(th)-layer neuron may include nneurons, n is a positive integer, and m and n may or may not be equal.There is a synapse weight parameter between the k^(th)-layer neuron andthe (k+1)^(th)-layer neuron. A signal output by the k^(th)-layer neuronis processed using the synapse weight parameter, and is input to the(k+1)^(th)-layer neuron.

Optionally, each k^(th)-layer neuron does not need to have a connectionrelationship with each (k+1)^(th)-layer neuron in the spiking neuralnetwork. In other words, at least one of the k^(th)-layer neuron mayalternatively have no connection relationship with at least one of the(k+1)^(th)-layer neuron.

In this embodiment of the present disclosure, there may be a pluralityof orders of synapse weight parameters between the k^(th)-layer neuronand the (k+1)^(th)-layer neuron. In other words, for any neuron y_(i)^(k) of the k^(th)-layer neuron and any neuron of the (k+1)^(th)-layerneuron that have a connection relationship, there may be q orders ofsynapse weight parameters between the two neurons, where the q orders ofsynapse weight parameters may be expressed as w_(ij1) ^((k,k+1)),w_(ij2) ^((k,k+1)), . . . , w_(ijq) ^((k,k+1)), and q is a positiveinteger greater than 2. In addition, when different values are chosenfor i and/or different values are chosen for j, corresponding q may beset to a same value or different values. For example, there may be qorders of synapse weight parameters between a first neuron y₁ ^(k) ofthe k^(th)-layer neuron and a first neuron y₁ ^(k+1) of the(k+1)^(th)-layer neuron, where q may be 5, that is, w₁₁₁ ^((k,k+1)),w₁₁₂ ^((k,k+1)), . . . , w₁₁₅ ^((k,k+1)). For another example, there maybe q orders of synapse weight parameters between an m^(th) neuron y_(m)^(k) of the k^(th)-layer neuron and an n^(th) neuron Y_(n) ^(k+1) of the(k+1)^(th)-layer neuron, where q may be 3, that is, w_(mn1) ^((k,k+1)),w_(mn2) ^((k,k+1)), . . . , w_(mn3) ^((k,k+1)).

In this embodiment of the present disclosure, the q orders of synapseweight parameters w_(ij1) ^((k,k+1)), w_(ij2) ^((k,k+1)), . . . ,w_(ijq) ^((k,k+1)) between any neuron y_(i) ^(k) of the k^(th)-layerneuron and any neuron y_(j) ^(k+1) of the (k+1)^(th)-layer neuron areused as an example for description. For ease of representation, w₁, w₂,. . . , w_(q)are used in this specfication to represent q orders ofsynapse weight parameters between neurons at any two adjacent layers.

FIG. 2 is a schematic flowchart of a signal processing method 100 basedon a spiking neural network according to an embodiment of the presentdisclosure. As shown in FIG. 2, the method 100 includes the followingsteps.

S110: Determine a first signal F₁(t) output by a first neuron.

S120: Process the first signal F₁(t) using q orders of synapse weightparameters w_(q)(t), w_(q−1)(t), . . . , w₁(t), to obtain a secondsignal F₂(t), where a speed at which an initial function w_(x+1) ⁰(t)met by an (x+1)^(th)-order synapse weight parameter of the q orders ofsynapse weight parameters attenuates with time t is higher than a speedat which an initial function w_(x) ⁰(t) met by an x^(th)-order synapseweight parameter attenuates with the time t, q is a positive integergreater than 1, and 1≤x≤q−1.

S130: Input the second signal F₂(t) to a second neuron, where the secondneuron is a next-layer neuron of the first neuron.

Therefore, in the signal processing method based on a spiking neuralnetwork in this embodiment of the present disclosure, an input signal isprocessed using a plurality of orders of synapse weight parameters, andinitial functions of the orders of the synapse weight parametersattenuate with time at different speeds such that a forgettingcharacteristic of the neural network is simulated, some unimportantinformation such as a secondary feature or background noise isforgotten, the neural network is more consistent with reality, featureextraction becomes easier, and a problem of an excessively largeinformation storage amount is further resolved.

In S110, the first signal F₁(t) output by the first neuron isdetermined. The first signal F₁(t) is a spike signal. A signal inputfrom the outside at any moment, that is, a signal needing to be input toa multilayer neural network, may be information such as an image, aword, or a sound that actually needs to be processed. Therefore, thesignal may be first converted, using a spike signal conversionapparatus, into a spike signal that can be identified by the multilayerneural network. For example, the information may be encoded, using aspike encoding circuit, into a standard spike signal that can beidentified by the multilayer neural network, and the spike signal isinput to the multilayer neural network at the moment, to be processed.

Therefore, for a neuron at any layer of the neural network, for example,the first neuron, the spike signal is input to the neuron, and theneuron is activated such that the neuron outputs the first signal F₁(t).F₁(t) is also a spike signal.

In this embodiment of the present disclosure, each neuron in the neuralnetwork executes a specific operation, and sends the spike signal in aparticular manner after processing the received spike signal.Optionally, a neuron may be a physical model. For example, a neuronphysical model may be a simple activation model, a leaky integrate andfire (LIF) model, a Hodgkin-Huxley ('HH) model, or the like. Thesemodels may all be implemented using corresponding physical circuits. Forexample, as shown in FIG. 1, any neuron y_(j) ^(k+1) of the(k+1)^(th)-layer neuron may receive an effect of each of neurons y₁ ^(k), y₂ ^(k), . . . , y_(l) ^(k) that are connected to y_(j) ^(k+1) andthat are of k^(th)-layer neurons, in other words, upper-layer neurons.y₁ ^(k), y₂ ^(k), . . . , y_(l) ^(k) are neurons that are connected toy_(j) ^(k+1) and that are of the k^(th)-layer neurons, and transferspike signals to the neuron y_(j) ^(k+1) using a corresponding synapseweight parameter. These spike signals are processed by the neuron y_(j)^(k+1) to obtain y_(j) ^(k+1)=f(y₁ ^(k), y₂ ^(k), . . . , y_(l) ^(k)),and y_(j) ^(k+1)=f(y₁ ^(k), y₂ ^(k), . . . , y_(l) ^(k)) is thentransferred to the next-layer neuron.

Therefore, the first signal F₁(t) output by the first neuron in theneural network is a spike signal that is output after the first neuronprocesses a signal input by an upper-layer neuron.

In S120, the first signal F₁(t) is processed using the q orders ofsynapse weight parameters w_(q)(t), w_(q−1)(t), . . . , w₁(t) to obtainthe second signal F₂(t). The speed at which the initial function w_(x+1)⁰(t) met by the (x+1)^(th)-order synapse weight parameter of the qorders of synapse weight parameters attenuates with time is higher thanthe speed at which the initial function w_(x) ⁰(t) met by thex^(th)-order synapse weight parameter attenuates with time, where q is apositive integer greater than 1, and 1≤x≤q−1.

It should be understood that the first signal F₁(t) is processed usingthe q orders of synapse weight parameters w_(q)(t), w_(q−1)(t), . . . ,w₁(t) When each order of synapse weight parameter of the q orders ofsynapse weight parameters is subject to no effect of a stimulationsignal, all the q orders of synapse weight parameters are set to meetcorresponding initial functions w_(q) ⁰(t), w_(q−1) ⁰(t), . . . , w₁⁰(t). The speed at which the initial function w_(x+1) ⁰(t) met by the(x+1)^(th)-order synapse weight parameter of the q orders of synapseweight parameters attenuates with time is higher than the speed at whichthe initial function w_(x) ⁰(t) met by the x^(th)-order synapse weightparameter attenuates with time. Optionally, an initial function w₁ ⁰(t)of a first-order synapse weight parameter of the q orders of synapseweight parameters is set to a function that does not attenuate withtime.

For example, as shown in FIG. 3, the first-order synapse weightparameter corresponds to a long-term memory effect, in other words, w₁⁰(t) does not attenuate with time. A second-order synapse weightparameter corresponds to a mid-and-long term memory effect, in otherwords, w₂ ⁰(t) slowly attenuates with time. By analogy, a q^(th)-ordersynapse weight parameter corresponds to an immediate memory effect, inother words, a speed at which w_(q) ⁰(t) attenuates with time is thelargest.

Optionally, when initial functions are set for the q orders of synapseweight parameters, a type of an initial function of each order ofsynapse weight parameter may be a nonlinear attenuation function such asan exponential function, a logarithmic function, or a step function, ormay be a linear attenuation function, or may be a combination of any twoor more of the foregoing function types. The initial functions of theorders of synapse weight parameters may be set to a same type, forexample, an exponential function. Alternatively, the initial functionsof the orders of synapse weight parameters may be set to differenttypes. For example, initial functions of some synapse weight parametersare linear attenuation functions, and initial functions of other synapseweight parameters are set to logarithmic functions. This is not limitedin this embodiment of the present disclosure.

In this embodiment of the present disclosure, after the first neuronoutputs the first signal F₁(t), all or some of the q orders of synapseweight parameters are activated with a stimulation effect of the firstsignal. For example, a moment t₁, in a time period within which thefirst neuron outputs the first signal F₁(t) is used as an example. Thatis, the first signal F₁(t) may include a first sub-signal F₁(t₁) outputby the first neuron at the moment t₁, and the first sub-signal F₁(t₁)may activate each order or some of the q orders of synapse weightparameters.

For a highest-order synapse weight parameter, that is, the q^(th)-ordersynapse weight parameter w_(q)(t₁), with a stimulation effect of thefirst sub-signal F₁ (t₁), w_(q)(t₁) meets the following formula (1):

w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (1)

where w_(q)(t₁−1) represents a q^(th)-order synapse weight parameter ata moment t₁−1, the moment t₁−1 is a previous moment of the moment t₁,and ΔF_(q)(t₁) is an update quantity determined based on a learning ruleand a stimulation effect, at the moment t₁, of the first sub-signalF₁(t₁) on the q^(th)-order synapse weight parameter w_(q)(t₁−1) at themoment t₁−1.

It should be understood that for a moment t₂ after the time periodwithin which the first neuron outputs the first signal F₁(t), assumingthat the moment t₂ is any moment at which there is no stimulation effectof the first signal F₁(t), the q^(th)-order synapse weight parametermeets an initial function w_(q) ⁰(t₂) at the moment t₂.

In this embodiment of the present disclosure, for other orders ofsynapse weight parameters except the highest-order synapse weightparameter, that is, an x^(th)-order synapse weight parameter w_(x)(t₁),where 1≤x≤q−1, with a stimulation effect of the first sub-signal F₁(t₁),w_(x)(t₁) meets the following formula (2):

w _(x)(t ₁)=w _(x)(t ₁−1)+ΔF _(x)(t ₁)+Δw _(x,x+1)(t ₁)+Δw _(x,x+2)(t₁)+ . . . +Δw _(x,q)(t ₁)  (2)

where w_(x)(t₁−1) represents an x^(th)-order synapse weight parameter atthe moment t₁−1, ΔF_(x)(t₁−1) is an update quantity determined based ona learning rule and a stimulation effect, at the moment t₁, of the firstsub-signal F₁(t₁) on the x^(th)-order synapse weight parameterw_(x)(t₁−1) at the moment t₁−1, Δw_(x,x+1)(t₁), Δw_(x,x+2)(t₁), . . . ,Δw_(x,q)(t₁) are respectively quantities of impact of an(x+1)^(th)-order synapse weight parameter, an (x+2)^(th)-order synapseweight parameter, . . . , and the q^(th)-order synapse weight parameterthat are at the moment t₁ on the x^(th)-order synapse weight parameterw_(x)(t₁).

It should be understood that when an (x+i)^(th)-order synapse weightparameter w_(x+i)(t₁) at the moment t₁ is greater than or equal to athreshold of an (x+i)^(th)-order synapse weight Δw_(x,x+i), (t₁) is not0 , or when an (x+i)^(th)-order synapse weight parameter w_(x+i)(t₁) atthe moment t₁ is less than a threshold of an (x+i)^(th)-order synapseweight, Δw_(x,x+i)(t₁) is equal to 0, and i=1, 2, . . . , or q−x.

The q^(th)-order synapse weight parameter changes due to the stimulationeffect of the first sub-signal F₁(t₁) at the moment t₁. When theq^(th)-order synapse weight parameter w_(q)(t₁) is greater than or equalto a threshold of a corresponding q^(th)-order synapse weight, theq^(th)-order synapse weight parameter may activate other (q−1) orders ofsynapse weight parameters, in other words, Δw_(x,q)(t₁) is not 0.Similarly, for any (x+i)^(th)-order synapse weight parameter w_(x+i)(t₁), when w_(x,x+i)(t₁) is greater than or equal to a threshold of an(x+i)^(th)-order synapse weight, Δw_(x,x+i)(t₁) is not 0.

Optionally, if the q^(th)-order synapse weight parameter changes due tothe stimulation effect of the first sub-signal F₁(t₁) at the moment t₁,but is less than a threshold of a corresponding q^(th)-order synapseweight, the q^(th)-order synapse weight parameter does not activateother (q−1) orders of synapse weight parameters, in other words,Δw_(x,q)(t₁) is equal to 0. Similarly, for any (x+i)^(th)-order synapseweight parameter w_(x+i)(t₁), when w_(x+i)(t₁) is less than a thresholdof an (x+i)^(th)-order synapse weight Δw_(x,x+i)(t₁) is 0.

In this embodiment of the present disclosure, a corresponding thresholdis set for each order of synapse weight parameter of the q orders ofsynapse weight parameters, and the threshold may be set according to anactual situation. No limitation is set thereto in this embodiment of thepresent disclosure.

It should be understood that for a moment t₂ after a time period withinwhich the first neuron outputs the first signal F₁(t), assuming that themoment t₂ is any moment at which there is no stimulation effect of thefirst signal F₁(t), a quantity ΔF_(x)(t₂) of impact of the first signalF₁(t) on the x^(th)-order synapse weight parameter at the moment t₂ is0. If quantities Δw_(x,x+1)(t₂), Δw_(x,x+2)(t₂), . . . , Δw_(x,q)(t₂) ofimpact of an (x+1)^(th)-order synapse weight parameter, an(x+2)^(th)-order synapse weight parameter, . . . , and the q^(th)-ordersynapse weight parameter on the x^(th)-order synapse weight parameterare not all 0 in this case, the x^(th)-order synapse weight parameterstill meets the formula (2) at the moment t₂, or if quantitiesΔw_(x,x+1)(t₂), Δw_(x,x+2)(t₂), . . . , Δw_(x,q)(t₂) of impact of an(x+1)^(th)-order synapse weight parameter, an (x+2)^(th)-order synapseweight parameter, . . . , and the q^(th)-order synapse weight parameteron the x^(th)-order synapse weight parameter are all equal to 0 in thiscase, the x^(th)-order synapse weight parameter meets an initialfunction w_(x) ⁰ (t₂) at the moment t₂.

In this embodiment of the present disclosure, the update quantityΔF_(q)(t₁) and the update quantity ΔF_(x)(t₁) are determined in theformulas (1) and (2) based on the learning rule and the first sub-signalF₁(t₁). The learning rule herein may be an existing related learningrule. For example, the learning rule may be a learning rule based on abiological feature such as an STDP learning rule or a Hebb learningrule. Alternatively, the learning rule may be a supervised learningalgorithm based on an error back propagation mechanism, for example, aSpikeProp learning rule, a QucikProp learning rule, a Tempotron learningrule, or an E-Learning learning rule. No limitation is set thereto inthis embodiment of the present disclosure.

It should be understood that the first sub-signal F₁(t₁) at the momentt₁ may be set to affect all or some of the q orders of synapse weightparameters. For example, the first sub-signal F₁(t₁) may be set toaffect only the q^(th)-order synapse weight parameter. In other words, aquantity ΔF_(q)(t₁) of impact of the first sub-signal F₁(t₁) on theq^(th)-order synapse weight parameter is not 0, and a quantityΔF_(x)(t₁) of impact of the first sub-signal F₁(t₁) on the x^(th)-ordersynapse weight parameter other than the q^(th)-order synapse weightparameter is 0. For another example, the first sub-signal F₁(t₁) mayalternatively be set to affect only the q^(th)-order synapse weightparameter to a (q−x)^(th)-order synapse weight parameter. In otherwords, ΔF_(q)(t₁), ΔF_(q−1)(t₁), . . . , and ΔF_(q−x)(t₁) are not 0, andΔF_(q−x−1)(t₁), ΔF_(q−x−2)(t₁), . . . , and ΔF₁(t₁) are 0.

Similarly, for the formula (2), the x^(th)-order synapse weightparameter is set to be related to each higher-order synapse weightparameter. That is, the (x+1)^(th)-order synapse weight parameteraffects each lower-order synapse weight parameter. In other words, the(x+1)^(th)-order synapse weight parameter affects the x^(th)-ordersynapse weight parameter to the first-order synapse weight parameter.Optionally, the (x+1)^(th)-order synapse weight parameter mayalternatively be set to affect only some lower-order synapse weightparameters. For example, the (x+1)^(th)-order synapse weight parametermay be set to affect only the x^(th)-order synapse weight parameter thatis one order lower than the (x+1)^(th)-order synapse weight parameter.In other words, the x^(th)-order synapse weight parameter is related tothe (x+1)^(th)-order synapse weight parameter. In other words,Δw_(x,x+2)(t₂), Δw_(x,x+3)(t₂), . . . , Δw_(x,q)(t₂) are all 0 in theformula (2).

For another example, the x^(th)-order synapse weight parameter mayalternatively be set to be related to the (x+1)^(th)-order synapseweight parameter to the (x+i)^(th)-order synapse weight parameter.Therefore, Δw_(x,x+i+1)(t₂ ), Δw_(x,x+i+2)(t₂), . . . , Δw_(x,q)(t₂) areall 0.

Preferably, in an embodiment, any moment, for example, a moment t₁, in atime period within which the first neuron outputs the first signal F₁(t)is used as an example. That is, the first signal F₁(t) includes thefirst sub-signal F₁(t₁) output by the first neuron at the moment t₁. Inthis case, the first sub-signal F₁(t₁) may be set to be capable ofactivating only the highest-order synapse weight parameter of the qorders of synapse weight parameters. In other words, the q^(th)-ordersynapse weight parameter w_(q)(t₁) still meets the formula (1) at themoment t₁. w_(q)(t₁−1) represents a q^(th)-order synapse weightparameter at a moment t₁−1, the moment t₁−1 is a previous moment of themoment t₁, and ΔF_(q)(t₁) is an update quantity determined based on alearning rule and a stimulation effect, at the moment t₁, of the firstsub-signal F₁(t₁), on the q^(th)-order synapse weight parameter,w_(q)(t₁−1) at the moment t₁−1.

However, for other orders of synapse weight parameters except thehighest-order synapse weight parameter, that is, the x^(th)-ordersynapse weight parameter w_(x)(t₁), where 1≤x≤q−1, the x^(th)-ordersynapse weight parameter w_(x)(t₁) may be set to be unrelated to thefirst sub-signal F₁(t₁), in other words, ΔF_(x)(t₁) in the formula (2)is set to 0. However, the x^(th)-order synapse weight parameterw_(x)(t₁) is related only to a value of the (x+1)^(th)-order synapseweight parameter, and a higher-order synapse weight parameter is set toaffect only a synapse weight parameter that is one order lower than thehigher-order synapse weight parameter.

When the (x+1)^(th) -order synapse weight parameter w_(x+1)(t₁) at themoment t₁ is less than a threshold of an (x+1)^(th)-order synapseweight, the x^(th)-order synapse weight parameter w_(x)(t₁) at themoment t₁ meets an initial function w_(x) ⁰(t₁). Alternatively, when the(x+1)^(th)-order synapse weight parameter w_(x+1)(t₁) at the moment t₁is greater than or equal to a threshold of an (x+1)^(th)-order synapseweight, the x^(th)-order synapse weight parameter w_(x)(t₁) at themoment t₁ meets a formula (3):

w _(x)(t ₁)=w _(x)(t ₁−1)+Δw _(x,x+1)(t ₁)  (3)

where w_(x)(t₁−1) represents an x^(th)-order synapse weight parameter atthe moment t₁−1, Δw_(x,x+1)(t₁) is a quantity of impact of the(x+1)^(th)-order synapse weight parameter at the moment t₁ on thex^(th)-order synapse weight parameter w_(x)(t₁).

FIG. 3 is used as an example, and when the first neuron outputs thefirst sub-signal F₁(t₁) at the moment t₁, the first sub-signal directlyacts on a q^(th) synapse connected to the first neuron, that is,activates an immediate memory q^(th)-order synapse weight parameterw_(q)(t₁). When the immediate memory w_(q)(t₁) is greater than or equalto a threshold of a corresponding q^(th)-order synapse weight, immediatememory that is one order lower than the immediate memory w_(q)(t₁) isactivated, that is, a (q−1)^(th) synapse weight parameter w_(q−1)(t₁)meets the formula (3). Another case can be obtained by analogy untillong-term memory is activated, in other words, the first synapse weightparameter is activated.

It should be understood that if the first signal F₁(t) no longer existsat any moment, for example, the moment t₂, but a higher-order synapseweight parameter w_(x+1)(t₂) is still greater than or equal to thethreshold of the corresponding (x+1)^(th)-order synapse weight,w_(x+1)(t₂) may still activate a synapse weight parameter that is oneorder lower than the higher-order synapse weight parameter w_(x+1)(t₂).In other words, the x^(th)-order synapse weight parameter w_(x)(t₂ )still meets the formula (3), and it is set that t₁=t₂.

In this embodiment of the present disclosure, q-order synapse weightsare determined based on the foregoing process, and the first signalF₁(t) output by the first neuron is processed using the q orders ofsynapse weight parameters w_(q)(t), w_(q−1)(t), . . . , w₁(t), to obtainthe second signal F₂(t). Optionally, the first signal F₁(t) may bemultiplied by the first-order synapse weight parameter w₁(t), to obtainthe second signal F₂(t). However, no limitation is set thereto in thisembodiment of the present disclosure.

In this embodiment of the present disclosure, any two neurons areconnected using a multi-order synapse. The multi-order synapse may bephysically implemented using one or more memristor units, or may bephysically implemented using a complex synapse circuit. FIG. 4 is usedas an example, and FIG. 4 shows an instance in which two neurons areconnected using one memristor unit. To implement a function of themulti-order synapse, the memristor unit also needs to have a multi-ordereffect. For example, two orders of synapse weight parameters are used asan example. Therefore, the memristor unit is a memristor unit having atwo-order effect.

As shown in FIG. 5, the first signal F₁(t) output by the first neuron y₁may be a spike signal shown in FIG. 5, and the spike signal acts on atwo-order memristor. The spike signal triggers a change in aconductivity value of the memristor. The process is a complex physicalprocess. A direct effect of the spike signal is to trigger a change inlocal temperature of the memristor. When the spike signal is removed,the local temperature promptly recovers to a balance state, for example,in a heat dissipation manner. This is equivalent to a short-term memoryeffect of a synapse weight parameter. That is, the change in thetemperature of the memristor corresponds to a change in a second-ordersynapse weight parameter. When accumulated local temperature isexcessively high, the conductivity value of the memristor is induced topermanently change. This is equivalent to a long-term memory effect of asynapse weight parameter. That is, a conductivity change in thememristor corresponds to a change in the first-order synapse weightparameter when the second-order synapse weight parameter exceeds athreshold. In this way, the single memristor unit may complete a synapseeffect corresponding to the two orders of synapse weight parameters.

In S130, the second signal F₂(t) is input to the second neuron, and thesecond neuron is the next-layer neuron of the first neuron. The firstneuron is used as a former neuron, and sends the spike signal, that is,the first signal, to the second neuron using the q-order synapses. Thesecond neuron is used as a latter neuron.

It should be understood that if the latter neuron is not in a last-layernetwork, that is, the latter neuron is not at an output layer, thesignal is transferred to a next-layer network. In this case, the latterneuron may be considered as a former neuron of the next-layer network. Asignal transfer manner is the same as the foregoing signal transfermanner. In other words, an original second neuron may be considered asthe first neuron, and a next neuron of the original second neuron is thesecond neuron. In addition, a spike signal input by an original latterneuron used as a former neuron not only includes a spike signal inputfrom the outside, but also includes a spike signal output by a previousnetwork. This part may be considered as cyclic network layering trainingof the spiking neural network. It should be noted that when a signal istransferred between networks, a time difference may exist.

In this embodiment of the present disclosure, if the second neuron is aneuron in the last-layer network, after the second signal F₂(t) is inputto the second neuron, the second neuron processes the second signal andoutputs a result. Correspondingly, an original signal of the signalbefore the signal enters the neural network may be information such asan image, a word, or a sound that actually needs to be processed, andmay be first converted, using a spike signal conversion apparatus, intoa spike signal that can be identified by the multilayer neural network,and then processed through the neural network. Similarly, afterprocessing the signal, the last-layer neuron may convert the spikesignal into actual information using the spike signal conversionapparatus and output the actual information such as an image, a word, ora sound. Optionally, the spike signal conversion apparatus may be aspike decoding circuit and convert the spike signal into informationsuch as an image, a word, or a sound.

Therefore, in the signal processing method based on a spiking neuralnetwork in this embodiment of the present disclosure, an input signal isprocessed using a plurality of orders of synapse weight parameters, andinitial functions of the orders of the synapse weight parametersattenuate with time at different speeds such that a forgettingcharacteristic of the neural network is simulated, some unimportantinformation such as a secondary feature or background noise isforgotten, the neural network is more consistent with reality, featureextraction becomes easier, and a problem of an excessively largeinformation storage amount is further resolved.

The signal processing method based on a spiking neural network in thisembodiment of the present disclosure is described below in detail usingseveral actual application scenarios as examples.

Embodiment 1

For example, as shown in FIG. 1, it is assumed that there are two ordersof synapse weight parameters (w₁, w₂) between any neuron of a y_(i) ^(k)of a k^(th) -layer neuron and any neuron y_(j) ^(k+1) of a(k+1)^(th)-layer neuron, w₁ is a first-order synapse weight parameter,and represents long-term memory, and an initial function of w₁ is set toa function that does not attenuate with time, and an initial value of w₁may be set to w₁ ⁰(t)=0 herein, in other words, the initial valueremains 0 when w₁ is subject to no stimulation effect, w₂ is asecond-order synapse weight parameter, and represents immediate memory,and an initial function of w₂ is set to a function that attenuates withtime, and is expressed as the following formula (4):

w ₂ ⁰(t)=w ₂(t ₀)exp[(t ₀ −t)/t ₀] (t≥t ₀)  (4)

where t₀ represents a moment at which a highest-order synapse weightparameter changes based on a related learning rule when signals of aformer neuron and a latter neuron act on a synapse weight parametertogether, and the highest-order synapse weight parameter herein is w₂,in addition, for t<t₀, the second-order synapse weight parameter may beset to 0.

The long-term memory first-order synapse weight parameter w₁ changeswhen and only when the immediate memory second-order synapse weightparameter w₂ is greater than or equal to a threshold of a second-ordersynapse weight. The long-term first-order synapse weight parameter w₁may be expressed as the following formula (5):

w ₁(t)=w ₁(t−1)+Δw ₁(t)

Δw ₁(t)=[w ₂(t)−7.5]*Δt w ₂(t)≥7.5

Δw ₁(t)=0 w ₂(t)<7.5  (5)

where 7.5 is the threshold of the second-order synapse weight.

Specific calculation values in FIG. 6 may be used as an example. Asshown in FIG. 6, when t<0.5, the initial value of the long-termfirst-order synapse weight parameter w₁ and an initial value of theimmediate memory second-order synapse weight parameter w₂ are both setto 0. When t=0.5, a former neuron outputs a signal to a synapse. With aneffect of the signal, the immediate memory second-order synapse weightparameter w₂ changes based on an STDP learning rule.

Because the threshold of the second-order synapse weight is set to 7.5,with a stimulation effect of an input signal, as shown in FIG. 6, theimmediate memory second-order synapse weight parameter w₂ exceeds thethreshold 7.5 when t=0.5. Therefore, when t=0.5, the first-order synapseweight parameter w₁ is activated, and a change in the first-ordersynapse weight parameter w₁ meets the formula (5).

When t=t+1, a next moment is entered, and the input signal is removed.Therefore, the immediate memory second-order synapse weight parameter w₂continuously attenuates based on a forgetting rule, in other words,continuously attenuates based on the specified initial function formula(4) with time, until the immediate memory second-order synapse weightparameter w₂ decreases to the initial value 0. At an initial stage ofremoving the stimulation signal, because the second-order synapse weightparameter w₂ is still greater than the threshold 7.5 set for thesecond-order synapse weight parameter w₂ , the long-term memoryfirst-order synapse weight parameter w₁ is continuously activated inthis case. Therefore, the first-order synapse weight parameter w₁continuously increases within a time period, until the immediate memorysecond-order synapse weight parameter is less than the threshold of theimmediate memory second-order synapse weight parameter. Therefore, thefirst-order synapse weight parameter w₁ no longer increases, meets thespecified initial function, and remains unchanged and no longerattenuates with time.

In this embodiment of the present disclosure, when stimulation signalsoutput by the first neuron are different, effects on a synapse aredifferent. In other words, changes corresponding to the orders ofsynapse weight parameters are different. For example, as shown in FIG.7, a period of a stimulation signal acting on a synapse is 2 s, and aperiod of a stimulation signal in FIG. 8 is 1 s. Therefore, acorresponding immediate memory second-order synapse weight parameter w₂increases during each time of stimulation, but quickly attenuates afterthe stimulation. When a next time of stimulation occurs, thesecond-order synapse weight parameter w₂ has not recovered to an initialvalue 0. Therefore, the second-order synapse weight parameter w₂increases again based on a current status value at a current moment. Inthis way, an accumulative effect is formed. Both the stimulation in FIG.7 and the stimulation in FIG. 8 are relatively weak, and a single timeof stimulation does not trigger a change in a long-term memoryfirst-order synapse weight parameter w₁. However, in FIG. 7, becausestimulation frequency is relatively low, continuous stimulation stilldoes not cause a second-order synapse weight parameter w₂ to exceed athreshold. Therefore, the change in the long-term memory first-ordersynapse weight parameter w₁ is not triggered. In other words, thefirst-order synapse weight parameter w₁ remains 0. However, when thestimulation frequency increases, as shown in FIG. 8, continuousstimulation may cause the second-order synapse weight parameter w₂ toexceed the threshold. Therefore, the change in the long-term memoryfirst-order synapse weight parameter w₁ is triggered. This explains thatfrequent weak stimulation and infrequent strong stimulation have a samefunction, and the foregoing process conforms to biological cognitivehabits. In other words, the multi-order neural network is closer tobiological cognitive habits.

Embodiment 2

For example, as shown in FIG. 1, it is assumed that there are threeorders of synapse weight parameters (w₁, w₂, w₃) between any neurony_(i) ^(k) of a k^(th)-layer neuron and any neuron y_(j) ^(k+1) of a(k+1)^(th)-layer neuron, w₁ is a first-order synapse weight parameter,and represents long-term memory, and an initial function of w₁ is setnot to attenuate with time, w₂ is a second-order synapse weightparameter, and represents short-term memory, and an initial function ofw₂ is set to relatively slowly attenuate with time, w₃ is a third-ordersynapse weight parameter, and represents immediate memory, and aninitial function of w₃ is set to quickly attenuate with time.

The initial functions of the three orders of synapse weight parameters(w₁, w₂, w₃) may be set to the following functions. For a highest-ordersynapse weight parameter, that is, the third-order synapse weightparameter w₃, it is assumed that the third-order synapse weightparameter w₃ is subject to an effect of a stimulation signal at a momentt₃. For example, the stimulation signal may be a stimulation signaloutput by a former neuron. The third-order synapse weight parameter w₃changes based on a learning rule. The third-order synapse weightparameter is no longer subject to new stimulation after the moment t₃.Therefore, the third-order synapse weight parameter w₃ quicklyattenuates with time based on the specified initial function. It isassumed that the initial function herein is set to the following formula(6):

w ₃ ⁰(t)=w ₃(t ₃)exp [(t₃ −t)/(d ₃ *t ₃)] (t≥t ₃)  (6)

where d₃ is an immediate memory attenuation factor, and d₃>0.

Similarly, for the second-order synapse weight parameter w₂, it isassumed that the second-order synapse weight parameter w₂ is notaffected by a stimulation signal output by a neuron, and is related onlyto the third-order synapse weight parameter w₃ that is one order higherthan the second-order synapse weight parameter w₂. Moreover, it isassumed that the third-order synapse weight parameter w₃ exceeds athreshold of a corresponding third-order synapse weight at a moment t₂such that the second-order synapse weight parameter w₂ can be activatedand change. After the moment t₂, the third-order synapse weightparameter w₃ is less than the threshold of the corresponding third-ordersynapse weight, and the second-order synapse weight parameter w₂ is nolonger activated such that the second-order synapse weight parameter w₂meets the initial function after the moment t₂, and relatively slowlyattenuates with time, and a speed at which the initial functionattenuates is less than a speed at which the third-order synapse weightparameter w₃ attenuates. Optionally, the initial function may be set tothe following formula (7):

w ₂ ⁰(t)=w ₂(t ₂)exp[(t ₂ −t)/(d ₂*t₂)] (t≥t ₂)  (7)

where d₂ is an immediate memory attenuation factor, and d₃>d₂>0.

Similarly, for a lowest-order synapse weight parameter, that is, thefirst-order synapse weight parameter w₁, it is assumed that thefirst-order synapse weight parameter w₁ is unrelated to both astimulation signal output by a neuron and a value of the third-ordersynapse weight parameter w₃, and is related only to the second-ordersynapse weight parameter w₂ that is one order higher than thefirst-order synapse weight parameter w₁. Moreover, at a moment t₁, thesecond-order synapse weight parameter w₂ exceeds a threshold of acorresponding second-order synapse weight such that the second-ordersynapse weight parameter w₂ can be activated and change. After themoment t₁, the second-order synapse weight parameter w₂ is less than thethreshold of the corresponding second-order synapse weight, and thefirst-order synapse weight parameter w₁ is no longer activated such thatthe first-order synapse weight parameter w₁ meets the initial functionafter the moment t₁, and does not change with time. Therefore, theinitial function of the first-order synapse weight parameter w₁ may beset to the following formula (8):

w ₁ ⁰(t)=w ₁ ⁰(t ₁) (t≥t ₁)  (8).

In this embodiment of the present disclosure, the three orders ofsynapse weight parameters (w₁, w₂, w₃) may be updated based on thefollowing rules. An update rule of the third-order synapse weightparameter w₃ may be set to the third-order synapse weight parameter w₃changes when and only when an external stimulation signal exists,otherwise, meets the formula (6). If the third-order synapse weightparameter w₃ is stimulated by a signal at a moment t, the update rule ofthe third-order synapse weight parameter w₃ may be expressed as thefollowing formula (9):

w ₃(t)=w ₃(t−1)+ΔF ₃(t)  (9)

where w₃(t−1) represents a value of the third-order synapse weightparameter at a previous moment, ΔF₃(t) represents an update quantitygenerated due to a stimulation effect of the stimulation signal on thethird-order synapse weight parameter. Optionally, the update quantityΔF₃(t) may be determined based on the learning rule and the stimulationsignal. For example, the learning rule may be an STDP learning rule. Asshown in FIG. 9, if information about a former neuron is generatedbefore an activity of a latter neuron, a connection between the neuronsis enhanced. Alternatively, if information about a former neuron isgenerated after an activity of a latter neuron, a connection between theneurons is weakened. ΔT represents a time difference of activities ofthe former neuron and the latter neuron. A value of ΔF₃(t) is related toan absolute value of the time difference ΔT. A smaller absolute valueindicates larger ΔF₃(t)

An update rule of the second-order synapse weight parameter w₂ may beset to the second-order synapse weight parameter w₂ changes when andonly when the third-order synapse weight parameter w₃ exceeds thethreshold of the corresponding third-order synapse weight, otherwise,meets the formula (7). The update rule of the second-order synapseweight parameter w₂ may be expressed as the following formula (10):

w ₂(t)=w ₂(t−1)+Δw ₂₃(t)   (10)

where w₂(t−1) represents a value of the second-order synapse weightparameter at a previous moment, Δw₂₃(t) is a quantity of impact of thethird-order synapse weight parameter w₃ on the second-order synapseweight parameter w₂ , and Δw₂₃(t) is related to the threshold of thethird-order synapse weight, for example, Δw₂₃(t) may be set to meet thefollowing formula (11):

Δw ₂₃(t)=∫(w ₃ −T ₃)dt  (11)

where T₃ represents the threshold of the third-order synapse weightcorresponding to the third-order synapse weight parameter w₃.

An update rule of the first-order synapse weight parameter w₁ may be setto the first-order synapse weight parameter w₂ changes when and onlywhen the second-order synapse weight parameter w₁ exceeds the thresholdof the corresponding second-order synapse weight, otherwise, meets theformula (8). The update rule of the first-order synapse weight parameterw₁ may be expressed as the following formula (12):

w ₁(t)=w ₁(t−1)+Δw ₁₂(t)  (12)

where w₁(t−1) represents a value of the first-order synapse weightparameter at a previous w₂ moment, Δw₁₂(t) is a quantity of impact ofthe second-order synapse weight parameter w₂ on the first-order synapseweight parameter w₁, and Δw₁₂(t) is related to the threshold of thesecond-order synapse weight, for example, Δw₁₂(t) may be set to meet thefollowing formula (13):

Δw ₁₂(t)=∫(w ₂ −T ₂)dt  (13)

where T₂ represents the threshold of the second-order synapse weightcorresponding to the second-order synapse weight parameter w₂.

Therefore, with a stimulation effect of a spike signal shown in FIG. 10,changes in the three orders of synapse weight parameters (w₁, w₂, w₃)may be shown in FIG. 10.

In this embodiment of the present disclosure, in addition to theforegoing embodiment, the method may be further applied to anotherapplication scenario. No limitation is set thereto in this embodiment ofthe present disclosure.

Optionally, in an embodiment, a function of the spiking neural networkhaving a forgetting characteristic in this embodiment of the presentdisclosure is described below using a specific application instance ofword memorization. For ease of description, a neural network having twoorders of synapse weight parameters is used herein as an example fordescription. Each word recitation may be considered as an input of aspike signal. It is assumed that one person recites a same word once oneach of a first day, a second day, a fourth day, a fifth day, and asixth day, in other words, a spike signal is input to the two-orderspiking neural network on each of the first day, the second day, thefourth day, the fifth day, and the sixth day, theoretically, afterreciting a new word, the person will quickly forget the word and cancommit the word to memory after repeated recitations. The process can beimplemented through simulation using the two-order spiking neuralnetwork in this embodiment of the present disclosure.

As shown in FIG. 11, recitation of a new word by a person may beconsidered as inputting a stimulation signal. Therefore, an immediatememory higher-order synapse weight parameter w₂ may be activated basedon a learning rule. However, w₂ does not exceed a threshold T₂corresponding to w₂. Therefore, a change in a long-term memoryfirst-order synapse weight parameter w₁ is not triggered. In addition,the immediate memory second-order synapse weight parameter w₂ attenuateswith time. However, the word is recited on the second day, the fourthday, the fifth day, and the sixth day again. In other words, newstimulation signals are input. As shown in FIG. 11, on the sixth day,the second-order synapse weight parameter w₂ exceeds the threshold T₂corresponding to w₂, and long-term memory of the person is triggered. Inother words, the first-order synapse weight parameter w₁ is activated.Therefore, the word can be permanently memorized.

Similarly, an application scenario such as hot news screening or keyinformation obtaining may also be implemented through simulation usingthe neural network having a plurality of orders of synapse weightparameters and having a forgetting characteristic in this embodiment ofthe present disclosure.

Therefore, in the signal processing method based on a spiking neuralnetwork in this embodiment of the present disclosure, an input signal isprocessed using a plurality of orders of synapse weight parameters, andinitial functions of the orders of the synapse weight parametersattenuate with time at different speeds such that a forgettingcharacteristic of the neural network is simulated, some unimportantinformation such as a secondary feature or background noise isforgotten, the neural network is more consistent with reality, featureextraction becomes easier, and a problem of an excessively largeinformation storage amount is further resolved. In addition, variousreal-time and complex problems such as mode identification, naturallanguage processing, and control and optimization may be completed usingthe neural network in this embodiment of the present disclosure, and thepresent disclosure has a wide application prospect. The spiking neuralnetwork having a forgetting characteristic gradually forgets learnedinformation such as a secondary feature or background noise, and aneventually stored feature is a most important feature of a thing, and ismore consistent with reality.

It should be understood that sequence numbers of the foregoing processesdo not mean execution sequences in the embodiments of the presentdisclosure. The execution sequences of the processes should bedetermined based on functions and internal logic of the processes, andshould not be construed as any limitation on the implementationprocesses of the embodiments of the present disclosure.

The signal processing method based on a spiking neural network in theembodiments of the present disclosure is described above in detail withreference to FIG. 1 to FIG. 11. Signal processing apparatuses based on aspiking neural network in the embodiments of the present disclosure aredescribed below with reference to FIG. 12 and FIG. 13.

As shown in FIG. 12, a signal processing apparatus 200 based on aspiking neural network in an embodiment of the present disclosureincludes a determining module 210, configured to determine a firstsignal F₁(t) output by a first neuron, a processing module 220,configured to process the first signal F₁(t) using q orders of synapseweight parameters w_(q)(t), w_(q−1)(t), . . . , w₁(t), to obtain asecond signal F₂(t), where a speed at which an initial function w_(x+1)⁰(t) met by an (x+1)^(th)-order synapse weight parameter of the q ordersof synapse weight parameters attenuates with time t is higher than aspeed at which an initial function w_(x) ⁰(t) met by an x^(th)-ordersynapse weight parameter attenuates with the time t, q is a positiveinteger greater than 1, and 1≤x≤q−1 , and an input module 230,configured to input the second signal F₂(t) to a second neuron, wherethe second neuron is a next-layer neuron of the first neuron.

Therefore, the signal processing apparatus based on a spiking neuralnetwork in this embodiment of the present disclosure processes an inputsignal using a plurality of orders of synapse weight parameters, andinitial functions of the orders of the synapse weight parametersattenuate with time at different speeds such that a forgettingcharacteristic of the neural network is simulated, some unimportantinformation such as a secondary feature or background noise isforgotten, the neural network is more consistent with reality, featureextraction becomes easier, and a problem of an excessively largeinformation storage amount is further resolved.

Optionally, in an embodiment, an initial function w₁ ⁰(t) of afirst-order synapse weight parameter of the q orders of synapse weightparameters does not attenuate with time.

Optionally, in an embodiment, the first signal F₁(t) includes a firstsub-signal F₁(t₁) output by the first neuron at a moment t₁, and aq^(th)-order synapse weight parameter w_(q)(t₁) at the moment t₁ meets aformula (1):

w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (1)

where w_(q)(t₁−1) represents a q^(th)-order synapse weight parameter ata moment t₁−1, the moment t₁−1 is a previous moment of the moment t₁,and ΔF_(q)(t₁) is an update quantity determined based on a learning ruleand a stimulation effect, at the moment t₁, of the first sub-signalF₁(t₁) on the q^(th)-order synapse weight parameter w_(q)(t₁−1) at themoment t₁−1, and an x^(th)-order synapse weight parameter w_(x)(t₁) atthe moment t₁ meets a formula (2):

w _(x)(t ₁)=w _(x)(t ₁−1)+ΔF _(x)(t ₁)+Δw _(x,x+1)(t ₁)+Δw _(x,x+2)(t₁)+ . . . +Δw _(x,q)(t ₁)  (2)

where w_(x)(t₁−1) represents an x^(th)-order synapse weight parameter atthe moment t₁−1, ΔF_(x)(t₁) is an update quantity determined based onthe learning rule and a stimulation effect, at the moment t₁, of thefirst sub-signal F₁(t₁) on the x^(th)-order synapse weight parameterw_(x)(t₁−1) at the moment t₁−1, Δw_(x,x+1)(t₁), Δw_(x,x+2)(t₁), . . . ,Δw_(x,q)(t₁) are respectively quantities of impact of an(x+1)^(th)-order synapse weight parameter, an (x+2)^(th)-order synapseweight parameter, . . . , and the q^(th)-order synapse weight parameterthat are at the moment t₁ on the x^(th)-order synapse weight parameterw_(x)(t₁).

Optionally, in an embodiment, when an (x+i)^(th)-order synapse weightparameter w_(x+i)(t₁) at the moment t₁ is greater than or equal to athreshold of an (x+i)^(th)-order synapse weight, Δw_(x,x+i)(t₁) is not 0, or when an (x+i)^(th)-order synapse weight parameter w_(x, x+i)(t₁) atthe moment t₁ is less than a threshold of an (x+i)^(th) synapse weight,Δw_(x,x+i)(t₁) is equal to 0, and i=1, 2, . . . , or q−x.

Optionally, in an embodiment, the first signal F₁(t) includes a firstsub-signal F₁(t₁) output by the first neuron at a moment t₁, and aq^(th)-order synapse weight parameter w_(q)(t₁) at the moment t₁ meets aformula (3):

w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (3)

where w_(q)(t₁−1) represents a q^(th)-order synapse weight parameter ata moment t₁−1, the moment t₁−1 is a previous moment of the moment t₁,and ΔF_(q)(t₁) is an update quantity determined based on a learning ruleand a stimulation effect, at the moment t₁, of the first sub-signalF₁(t₁) on the q^(th)-order synapse weight parameter w_(q)(t₁−1) at themoment t₁−1, andwhen an (x+1)^(th)-order synapse weight parameter w_(x+1)(t₁) at themoment t₁ is greater than or equal to a threshold of an (x+1)^(th)-ordersynapse weight, an x^(th)-order synapse weight parameter w_(x)(t₁) atthe moment t₁ meets a formula (4):

w _(x)(t ₁)=w _(x)(t ₁−1)+Δw _(x,x+1)(t ₁)  (4)

where w_(x)(t₁−1) represents an x^(th)-order synapse weight parameter atthe moment t₁−1, Δw_(x,x+1)(t₁) is a quantity of impact of the(x+1)^(th)-order synapse weight parameter at the moment t₁ on thex^(th)-order synapse weight parameter w_(x)(t₁), orwhen an (x+1)^(th)-order synapse weight parameter w_(x+1)(t₁) at themoment t₁ is less than a threshold of an (x+1)^(th)-order synapseweight, an x^(th)-order synapse weight parameter w_(x)(t₁) at the momentt₁ meets an initial function w_(x) ⁰(t₁).

Optionally, in some embodiments, the learning rule is a learning rulebased on a biological feature or a supervised learning rule based on anerror back propagation mechanism.

Optionally, in an embodiment, the processing module 220 is configured todetermine a product of the first signal F₁(t) and the first-ordersynapse weight parameter w₁(t) as the second signal F₂(t).

It should be understood that the foregoing operations and/or functionsof modules in the signal processing apparatus 200 in this embodiment ofthe present disclosure are intended for implementing correspondingprocedures of the methods in FIG. 1 to FIG. 11. For brevity, details arenot described herein again.

It should be further understood that the determining module 210 and theprocessing module 220 in this embodiment of the present disclosure maybe implemented using a processor or a processor-related circuitcomponent, and the input module 230 may be implemented using atransceiver or a transceiver-related circuit component.

As shown in FIG. 13, an embodiment of the present disclosure furtherprovides a signal processing apparatus 300 based on a spiking neuralnetwork. The signal processing apparatus 300 includes a processor 310, amemory 320, a bus system 330, and a transceiver 340. The processor 310,the memory 320, and the transceiver 340 are connected using the bussystem 330. The memory 320 is configured to store an instruction. Theprocessor 310 is configured to execute the instruction stored in thememory 320, to control the transceiver 340 to receive and send a signal.When the processor 310 executes the instruction stored in the memory320, the processor 310 is configured to determine a first signal F₁(t)output by a first neuron, and process the first signal F₁(t) using qorders of synapse weight parameters w_(q)(t), w_(q−1)(t), . . . , w₁(t),to obtain a second signal F₂(t). A speed at which an initial functionw_(x+1) ⁰(t) met by an (x+1)^(th)-order synapse weight parameter of theq orders of synapse weight parameters attenuates with time t is higherthan a speed at which an initial function w_(x) ⁰(t) met by anx^(th)-order synapse weight parameter attenuates with the time t, q is apositive integer greater than 1, and 1≤x≤q−1. The transceiver 340 isconfigured to input the second signal F₂(t) to a second neuron, wherethe second neuron is a next-layer neuron of the first neuron.

Therefore, the signal processing apparatus based on a spiking neuralnetwork in this embodiment of the present disclosure processes an inputsignal using a plurality of orders of synapse weight parameters, andinitial functions of the orders of the synapse weight parametersattenuate with time at different speeds such that a forgettingcharacteristic of the neural network is simulated, some unimportantinformation such as a secondary feature or background noise isforgotten, the neural network is more consistent with reality, featureextraction becomes easier, and a problem of an excessively largeinformation storage amount is further resolved.

Optionally, in an embodiment, an initial function w₁ ⁰(t) of afirst-order synapse weight parameter of the q orders of synapse weightparameters does not attenuate with time.

Optionally, in an embodiment, the first signal F₁(t) includes a firstsub-signal F₁(t₁) output by the first neuron at a moment t₁, and aq^(th)-order synapse weight parameter w_(q)(t₁) at the moment t₁ meets aformula (1):

w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (1)

where w_(q)(t₁−1) represents a q^(th)-order synapse weight parameter ata moment t₁−1, the moment t₁−1 is a previous moment of the moment t₁,and ΔF_(q)(t₁) is an update quantity determined based on a learning ruleand a stimulation effect, at the moment t₁, of the first sub-signalF₁(t₁) on the q^(th)-order synapse weight parameter w_(q)(t₁−1) at themoment t₁−1, and an x^(th)-order synapse weight parameter w_(x)(t₁) atthe moment t₁ meets a formula (2):

w _(x)(t ₁)=w _(x)(t ₁−1)+ΔF _(x)(t ₁)+Δw _(x,x+1)(t ₁)+Δw _(x,x+2)(t₁)+ . . . +Δw _(x,q)(t ₁)  (2)

where w_(x)(t₁−1) represents an x^(th)-order synapse weight parameter atthe moment t₁−1, ΔF_(x)(t₁) is an update quantity determined based onthe learning rule and a stimulation effect, at the moment t₁, of thefirst sub-signal F₁(t₁) on the x^(th)-order synapse weight parameterw_(x)(t₁−1) at the moment t₁−1, Δw_(x,x+1)(t₁), Δw_(x,x+2)(t₁), . . . ,Δw_(x,q)(t₁) are respectively quantities of impact of an(x+1)^(th)-order synapse weight parameter, an (x+2)^(th)-order synapseweight parameter, . . . , and the q^(th)-order synapse weight parameterthat are at the moment t₁ on the x^(th)-order synapse weight parameterw_(x)(t₁).

Optionally, in an embodiment, when an (x+i)^(th)-order synapse weightparameter w_(x+i)(t₁) at the moment t₁ is greater than or equal to athreshold of an (x+i)^(th)-order synapse weight, Δw_(x,x+i)(t₁) is not0, or when an (x+i)^(th)-order synapse weight parameter at w_(x+i)(t₁)at the moment t₁ is less than a threshold of an (x+i)^(th) synapseweight, Δw_(x,x+i)(t₁) is equal to 0, and i=1, 2, . . . , or q−x.

Optionally, in an embodiment, the first signal F₁(t) includes a firstsub-signal F₁(t₁) output by the first neuron at a moment t₁, and aq^(th)-order synapse weight parameter w_(q)(t₁) at the moment t₁ meets aformula (3):

w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (3)

where w_(q)(t₁−1) represents a q^(th)-order synapse weight parameter ata moment t₁−1, the moment t₁−1 is a previous moment of the moment t₁,and ΔF_(q)(t₁) is an update quantity determined based on a learning ruleand a stimulation effect, at the moment t₁, of the first sub-signalF₁(t₁) on the q^(th)-order synapse weight parameter w_(q)(t₁−1) at themoment t₁−1, and when an (x+1)^(th)-order synapse weight parameterw_(x+1)(t₁) at the moment t₁ is greater than or equal to a threshold ofan (x+1)^(th)-order synapse weight, an x^(th)-order synapse weightparameter w_(x)(t₁) at the moment t₁ meets a formula (4):

w _(x)(t ₁)=w _(x)(t ₁−1)+Δw _(x,x+1)(t ₁)  (4)

where w_(x)(t₁−1) represents an x^(th)-order synapse weight parameter atthe moment t₁−1, Δw_(x,x+1)(t₁) is a quantity of impact of the(x+1)^(th)-order synapse weight parameter at the moment t₁ on thex^(th)-order synapse weight parameter w_(x)(t₁), or when an(x+1)^(th)-order synapse weight parameter w_(x+1)(t₁) at the moment t₁is less than a threshold of an (x+1)^(th)-order synapse weight, anx^(th)-order synapse weight parameter w_(x)(t₁) at the moment t₁ meetsan initial function w_(x) ⁰(t₁).

Optionally, in an embodiment, the learning rule is a learning rule basedon a biological feature or a supervised learning rule based on an errorback propagation mechanism.

Optionally, in an embodiment, the processor 310 is configured todetermine a product of the first signal F₁(t) and the first-ordersynapse weight parameter w₁(t) as the second signal F₂(t).

It should be understood that the signal processing apparatus 300 basedon a spiking neural network in this embodiment of the present disclosurecan correspond to the signal processing apparatus 200 based on a spikingneural network in the embodiment of the present disclosure, and theforegoing operations and/or functions of the modules in the signalprocessing apparatus 300 are intended for implementing correspondingprocedures of the methods in FIG. 1 to FIG. 11. For brevity, details arenot described herein again.

It should be further understood that in this embodiment of the presentdisclosure, the processor may be a central processing unit (CPU), or maybe another general purpose processor, a digital signal processor (DSP),an application-specific integrated circuit (ASIC), a field programmablegate array (FPGA) or another programmable logic device, a discrete gateor a transistor logic device, a discrete hardware component, or thelike. The general purpose processor may be a microprocessor, or theprocessor may be any conventional processor or the like.

It should be further understood that the memory in this embodiment ofthe present disclosure may be a volatile memory or a non-volatilememory, or may include a volatile memory and a non-volatile memory. Thenon-volatile memory may be a read-only memory (ROM), a programmableread-only memory (PROM), an erasable programmable read-only memory(EPROM), an electrically erasable programmable read-only memory(EEPROM), or a flash memory. The volatile memory may be a random accessmemory (RAM), and is used as an external cache. By way of example butnot limitation, a plurality of forms of RAMs can be used, for example, astatic random access memory (SRAM), a dynamic random access memory(DRAM), a synchronous dynamic random access memory (SDRAM), a doubledata rate synchronous dynamic random access memory (DDR SDRAM), anenhanced synchronous dynamic random access memory (ESDRAM), a synchlinkdynamic random access memory (SLDRAM), and a direct Rambus random accessmemory (DR RAM).

It should be noted that, when the processor is a general purposeprocessor, a DSP, an ASIC, an FPGA or another programmable logic device,a discrete gate or a transistor logic device, or a discrete hardwarecomponent, the memory (or a storage module) is integrated to theprocessor.

It should be noted that the memory of the system and the method that aredescribed in the specification is to include, but is not limited to,these and any other appropriate types of memories.

It should be further understood that in this embodiment of the presentdisclosure, the bus system may include a power bus, a control bus, astatus signal bus, and the like, in addition to a data bus. However, forclear description, various types of buses in FIG. 13 are marked as thebus system.

In an implementation process, steps in the foregoing methods can beimplemented using a hardware integrated logical circuit in theprocessor, or using instructions in a form of software. The steps of themethod disclosed with reference to the embodiments of the presentdisclosure may be directly performed by a hardware processor, or may beperformed using a combination of hardware in the processor and asoftware module. A software module may be located in a mature storagemedium in the art such as a random access memory, a flash memory, aread-only memory, a programmable read-only memory, an electricallyerasable programmable memory, a register, or the like. The storagemedium is located in the memory, and a processor reads information inthe memory and completes the steps in the foregoing methods incombination with hardware of the processor. To avoid repetition, detailsare not described herein again.

It should be further understood that the numbers in the specificationare used for differentiation for ease of description, and are not usedfor limiting the scope of the embodiments of the present disclosure.

It should be understood that sequence numbers of the foregoing processesdo not mean execution sequences in some embodiments. The executionsequences of the processes should be determined based on functions andinternal logic of the processes, and should not be construed as anylimitation on the implementation processes of the embodiments of thepresent disclosure.

A person of ordinary skill in the art may be aware that, modules inexamples described in combination with the embodiments disclosed in thisspecification may be implemented by electronic hardware or a combinationof computer software and electronic hardware. Whether the functions areperformed by hardware or software depends on particular applications anddesign constraint conditions of the technical solutions. A personskilled in the art may use different methods to implement the describedfunctions for each particular application, but it should not beconsidered that the implementation goes beyond the scope of the presentdisclosure.

In the several embodiments provided in this application, it should beunderstood that the disclosed apparatus and method may be implemented inother manners. For example, the described apparatus embodiment is merelyan example. For example, the module division is merely logical functiondivision and there may be another division manner in actualimplementation. For example, a plurality of modules may be combined orintegrated to another module, or some features may be ignored or notperformed.

In addition, functional modules in the apparatus embodiments may beintegrated to one processing unit, or the functional modules mayphysically exist in respective processing units, or two or morefunctional modules are integrated to one processing unit.

The foregoing descriptions are merely specific implementations of thisapplication, but are not intended to limit the protection scope of thisapplication. Any variation or replacement readily figured out by aperson skilled in the art within the technical scope disclosed in theembodiments of the present disclosure shall fall within the protectionscope of this application. Therefore, the protection scope of thisapplication shall be subject to the protection scope of the claims.

what is claimed is:
 1. A signal processing method implemented in aspiking neural network, comprising: receiving, by a memristor, a firstsignal F₁(t) output by a first neuron; processing, by the memristor, thefirst signal F₁(t) using q orders of synapse weight parameters w_(q)(t),w_(q−1)(t), . . . , w₁(t) to obtain a second signal F₂(t), wherein aspeed at which an initial function w_(x+1) ⁰(t) met by an(x+1)^(th)-order synapse weight parameter of the q orders of synapseweight parameters attenuates with time t is higher than a speed at whichan initial function w_(x+1) ⁰(t) met by an (x+1)^(th)-order synapseweight parameter attenuates with the time t, wherein q is a positiveinteger greater than 1, and wherein 1≤x≤q−1; and outputting, by thememristor, the second signal F₂(t) to a second neuron, wherein thesecond neuron is a next-layer neuron of the first neuron.
 2. The signalprocessing method according to claim 1, wherein an initial function w₁⁰(t) of a first-order synapse weight parameter of the q orders ofsynapse weight parameters does not attenuate with time.
 3. The signalprocessing method according to claim 1, wherein the first signal F₁(t)comprises a first sub-signal F₁(t₁) output by the first neuron at amoment t₁, and a q^(th)-order synapse weight parameter w_(q)(t₁) at themoment t₁ meets a condition (1):w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (1) wherein w_(q)(t₁−1)represents a q^(th)-order synapse weight parameter at a moment t₁−1, themoment t₁−1 is a previous moment of the moment t₁, and ΔF_(q)(t₁) is anupdate quantity determined based on a learning rule and a stimulationeffect at the moment t₁ of the first sub-signal F₁(t₁) on theq^(th)-order synapse weight parameter w_(q)(t₁−1) at the moment t₁−1,and wherein an x^(th)-order synapse weight parameter w_(x)(t₁) at themoment t₁ meets a condition (2):w _(x)(t ₁)=w _(x)(t ₁−1)+ΔF _(x)(t ₁)+Δw _(x,x+1)(t ₁)+Δw _(x,x+2)(t₁)+ . . . +Δw _(x,q)(t ₁)  (2) wherein w_(x)(t₁−1) represents anx^(th)-order synapse weight parameter at the moment t₁−1; ΔF_(x)(t₁) isan update quantity determined based on the learning rule and thestimulation effect at the moment t₁ of the first sub-signal F₁(t₁) onthe x^(th)-order synapse weight parameter w_(x)(t₁−1) at the momentt₁−1, wherein Δw_(x,x+1)(t₁), Δw_(x,x+2)(t₁), . . . , Δw_(x,q)(t₁) arerespectively quantities of impact of an (x+1)^(th)-order synapse weightparameter, an (x+2)^(th)-order synapse weight parameter, . . . , and theq^(th)-order synapse weight parameter that are at the moment t₁ on thex^(th)-order synapse weight parameter w_(x)(t₁).
 4. The signalprocessing method according to claim 3, wherein an (x+i)^(th)-ordersynapse weight parameter w_(x+i)(t₁) at the moment t₁ is greater than orequal to a threshold of an (x+i)^(th)-order synapse weight, and whereinΔw_(x,x+i)(t₁) is not
 0. 5. The signal processing method according toclaim 3, wherein an (x+i)^(th)-order synapse weight parameterw_(x+i)(t₁) at the moment t₁ is less than a threshold of an(x+i)^(th)-order synapse weight, wherein Δw_(x,x+i)(t₁) is equal to 0,and wherein i=1, 2, . . . , or q−x.
 6. The signal processing methodaccording to claim 3, wherein the learning rule is a learning rule basedon a biological feature or a supervised learning rule based on an errorback propagation mechanism.
 7. The signal processing method according toclaim 1, wherein the first signal F₁(t) comprises a first sub-signalF₁(t₁) output by the first neuron at a moment t₁, and a q^(th)-ordersynapse weight parameter w_(q)(t₁)at the moment t₁ meets a condition(3):w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (3) wherein w_(q)(t₁−1)represents a q^(th)-order synapse weight parameter at a moment t₁−1, themoment t₁−1 is a previous moment of the moment t₁, and ΔF_(q)(t₁) is anupdate quantity determined based on a learning rule and a stimulationeffect at the moment t₁ of the first sub-signal F₁(t₁) on theq^(th)-order synapse weight parameter w_(q)(t₁−1) at the moment t₁−1. 8.The signal processing method according to claim 6, wherein an(x+1)^(th)-order synapse weight parameter w_(x+1)(t₁) at the moment t₁is greater than or equal to a threshold of an (x+1)^(th)-order synapseweight, wherein an x^(th)-order synapse weight parameter w_(x)(t₁) atthe moment t₁ meets a condition (4):w _(x)(t ₁)=w _(x)(t ₁−1)+Δw _(x,x+1)(t ₁)  (4) wherein w_(x)(t₁−1)represents an x^(th)-order synapse weight parameter at the moment t₁−1,and wherein Δw_(x,x+1)(t₁) is a quantity of impact of the(x+1)^(th)-order synapse weight parameter at the moment t₁ on thex^(th)-order synapse weight parameter w_(x)(t₁).
 9. The signalprocessing method according to claim 6, wherein an (x+1)^(th)-ordersynapse weight parameter w_(x+1)(t₁) at the moment t₁ is less than athreshold of an (x+1)^(th)-order synapse weight, and wherein an x^(th)-order synapse weight parameter w_(x)(t₁) at the moment t₁ meets aninitial function w_(x) ⁰(t₁).
 10. The signal processing method accordingto claim 1, wherein processing, by the memristor, the first signal F₁(t)using the q orders of synapse weight parameters w_(q)(t), w_(q−1)(t), .. . , w₁(t) to obtain a second signal F₂(t) comprises determining, bythe memristor, a product of the first signal F₁(t) and a first-ordersynapse weight parameter w₁(t) as the second signal F₂(t).
 11. A spikingneural network, comprising: a first neuron configured to output a firstsignal F₁(t); a memristor coupled to the first neuron and configured to:receive the first signal F₁(t) output by the first neuron; process thefirst signal F₁(t) using q orders of synapse weight parameters w_(q)(t),w_(q−1)(t), . . . , w₁(t) to obtain a second signal F₂(t), wherein aspeed at which an initial function w_(x+1) ⁰(t) met by an(x+1)^(th)-order synapse weight parameter of the q orders of synapseweight parameters attenuates with time t is higher than a speed at whichan initial function w_(x) ⁰(t) met by an x^(th)-order synapse weightparameter attenuates with the time t, wherein q is a positive integergreater than 1, and wherein 1≤x≤q−1; and output the second signal F₂(t);and a second neuron coupled to the memristor and configured to receivethe second signal F₂(t), wherein the second neuron is a next-layerneuron of the first neuron.
 12. The spiking neural network according toclaim 11, wherein an initial function w₁ ⁰(t) of a first-order synapseweight parameter of the q orders of synapse weight parameters does notattenuate with time.
 13. The spiking neural network according to claim11, wherein the first signal F₁(t) comprises a first sub-signal F₁(t₁)output by the first neuron at a moment t₁, and a q^(th)-order synapseweight parameter w_(q)(t₁) at the moment t₁ meets a condition (1):w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (1) wherein w_(q)(t₁−1)represents a q^(th)-order synapse weight parameter at a moment t₁−1, themoment t₁−1 is a previous moment of the moment t₁, and ΔF_(q)(t₁) is anupdate quantity determined based on a learning rule and a stimulationeffect at the moment t₁ of the first sub-signal F₁(t₁) on theq^(th)-order synapse weight parameter w_(q)(t₁−1) at the moment t₁−1,and wherein an x^(th)-order synapse weight parameter w_(x)(t₁) at themoment t₁ meets a condition (2):w _(x)(t ₁)=w _(x)(t ₁−1)+ΔF _(x)(t ₁)+Δw _(x,x+1)(t ₁)+Δw _(x,x+2)(t₁)+ . . . +Δw _(x,q)(t ₁)  (2) wherein w_(x)(t₁−1) represents anx^(th)-order synapse weight parameter at the moment t₁−1; ΔF_(x)(t₁) isan update quantity determined based on the learning rule and thestimulation effect, at the moment t₁, of the first sub-signal F₁(t₁) onthe x^(th)-order synapse weight parameter w_(x)(t₁−1) at the momentt₁−1, wherein Δw_(x,x+1)(t₁), Δw_(x,x+2)(t₁), . . . , Δw_(x,q)(t₁) arerespectively quantities of impact of an (x+1)^(th)-order synapse weightparameter, an (x+2)^(th)-order synapse weight parameter, . . . , and theq^(th)-order synapse weight parameter that are at the moment t₁ on thex^(th)-order synapse weight parameter w_(x)(t₁).
 14. The spiking neuralnetwork according to claim 13, wherein an (x+i)^(th)-order synapseweight parameter w_(x+i)(t₁) at the moment t₁ is greater than or equalto a threshold of an (x+i)^(th)-order synapse weight, and whereinΔw_(x,x+i)(t₁) is not
 0. 15. The spiking neural network according toclaim 13, wherein an (x+i)^(th)-order synapse weight parameterw_(x+i)(t₁) at the moment t₁ is less than a threshold of an(x+i)^(th)-order synapse weight, wherein Δw_(x,x+i)(t₁) is equal to 0,and wherein i=1, 2, . . . , or q−x.
 16. The spiking neural networkaccording to claim 11, wherein the learning rule is a learning rulebased on a biological feature or a supervised learning rule based on anerror back propagation mechanism.
 17. The spiking neural networkaccording to claim 16, wherein the first signal F₁(t) comprises a firstsub-signal F₁(t₁) output by the first neuron at a moment t₁, and aq^(th)-order synapse weight parameter w_(q)(t₁) at the moment t₁ meets acondition (3):w _(q)(t ₁)=w _(q)(t ₁−1)+ΔF _(q)(t ₁)  (3) wherein w_(q)(t₁−1)represents a q^(th)-order synapse weight parameter at a moment t₁−1, themoment t₁−1 is a previous moment of the moment t₁, and ΔF_(q)(t₁) is anupdate quantity determined based on a learning rule and a stimulationeffect at the moment t₁ of the first sub-signal F₁(t₁) on theq^(th)-order synapse weight parameter w_(q)(t−1) at the moment t₁−1. 18.The spiking neural network according to claim 16, wherein an(x+1)^(th)-order synapse weight parameter w_(x+1)(t₁) at the moment t₁is greater than or equal to a threshold of an (x+1)^(th)-order synapseweight, wherein an x^(th)-order synapse weight parameter w_(x)(t₁) atthe moment t₁ meets a condition (4):w _(x)(t ₁)=w _(x)(t ₁−1)+Δw _(x,x+1)(t ₁)  (4) wherein w_(x)(t₁−1)represents an x^(th)-order synapse weight parameter at the moment t₁−1 ,and wherein Δw_(x,x+1)(t₁) is a quantity of impact of the(x+1)^(th)-order synapse weight parameter at the moment t₁ on thex^(th)-order synapse weight parameter w_(x)(t₁).
 19. The spiking neuralnetwork according to claim 13, wherein an (x+1)^(th)-order synapseweight parameter w_(x+1)(t₁) at the moment t₁ is less than a thresholdof an (x+1)^(th)-order synapse weight, and wherein an x^(th) -ordersynapse weight parameter w_(x)(t₁) at the moment t₁ meets an initialfunction w_(x) ⁰(t₁).
 20. The spiking neural network according to claim11, wherein the memristor is further configured to determine a productof the first signal F₁(t) and a first-order synapse weight parameterw₁(t) as the second signal F₂(t).